Bibliographic record
Abstract
Tourism is big business in British Columbia a $13.4 billion dollar industry. A small but fast emerging segment is aboriginal tourism. Although aboriginal cultural tourism accounts for a mere 0.3% of the market it translates to a $40 million dollar sector. In six short years aboriginal tourism has doubled from a $20 million dollar segment in 2006 to a $42 million dollar segment in 2012. It's no wonder a planned growth initiative is being spearheaded by Aboriginal Tourism BC and the provincial government. Expectations are ten percent annually, totally $68 million in 2017. Thirty-two kilometers west of Smithers is Moricetown, home to the Witsuwit'en people. The valley around the Moricetown canyon was once a traditional fishing ground visited by five clans of the area. Today, Moricetown continues to be a popular destination with hundreds of visitors lining the banks of the canyon eager to see the spawning salmon and traditional fishing methods. With the increased popularity of cultural tourism it is important that Moricetown market itself effectively to earn its share of this profitable and growing sector. Without a marketing plan to date, the band's marketing activities have been sporadic and without measure. This paper examines Moricetown's history and culture, its current marketing mix tourism product offerings, price, place and promotional activities questioning how Moricetown can capitalize on the culture to exploit its tourism opportunities. Through literature review, focus groups, stakeholder and tourist interviews, the research data collected helps to refine the target market and segments. Other tools used to examine the market include various strategy models such as PESTEL SWOT and VRIE. These serve to clarify competitive advantage and analyze the product-market fit. In the case of aboriginal tourism sites, those in the target market want to experience what it was like for aboriginal people before contact. The delivery of a valuable aboriginal tourism product that
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".